Method and system for smart road departure warning and navigation assist in inclement weather with low visibility

ABSTRACT

A method of operating a vehicle of determining whether the vehicle is operating in a road segment with a low visibility condition to cause a loss of input of sensor data to a vehicle controller that operates an assist feature, activating one or more adaptive alerts based on a road departure risk of the vehicle, and driver use of the assist feature in the upcoming road segment, wherein the road departure risk is determined by calculating a road departure risk index that compares an estimated vehicle path based on the vehicle state data with a probabilistic vehicle path for the upcoming road segment; and predicting whether will operate within an acceptable path in the upcoming road segment; and tracking the vehicle in the upcoming road segment based on vehicle navigation data to provide at least one adaptive alert based on a prediction of the road departure risk.

INTRODUCTION

The present disclosure generally relates to autonomous andsemi-autonomous vehicles and more particularly relates to methods andsystems for adaptive driver notifications in low visibility operatingenvironment using positioning and risk index formulations to adapt lanedeparture warning and lane-keeping assist features based on driver useand preferences, vehicle states, and inclement weather conditions.

Advanced driver assistance systems (ADAS) capabilities such as lanedeparture warning, blind-spot detection, and the emergency braking arecommonplace and provided by manufacturers today. Further, more advancedsemi-autonomous ADAS (S-ADAS) and autonomous vehicles functionalitiesare in various stages of rollout by manufacturers and includecapabilities of sensing and navigating a vehicular environment withlittle or no user input. Adverse weather creates challenges with ADASfeatures to which automated vehicles and drivers must respond.

Vehicle automation has been categorized into numerical levels rangingfrom Level Zero, corresponding to no automation with full human control,to Level Five, corresponding to full automation with no human control.Various automated driver-assistance systems, such as cruise control,adaptive cruise control, and parking assistance systems correspond tolower automation levels, while true “driverless” vehicles correspond tohigher automation levels. Even though autonomous vehicles have madegreat advances in recent years, designers continue to seek improvements,particularly with respect to navigation functionality such as trajectoryplanning.

Accordingly, it is desirable to provide systems and methods that enableadvanced adaptive driver notification based on road departure risks ininclement weather causing low visibility driving conditions leading to aloss of camera data to the vehicle controller that impacts driverassistance features such as lane departure warning and lane-keepassistance.

It is desirable to enable adaptive driver notification of driver-assistfeatures based on driver preferences, vehicle states, and operatingenvironment conditions when actuating or using autonomous orsemi-autonomous modes of vehicular operations, particularly drivernotification methods and systems that address losses of input of sensordata in inclement weather driving conditions.

Furthermore, other desirable features and characteristics of the systemsand methods will become apparent from the subsequent detaileddescription and the appended claims, taken in conjunction with theaccompanying drawings and the foregoing technical field and background.

SUMMARY

A system is disclosed for providing adaptive notifications to a driverwhen operating a vehicle in a low visibility condition that causes aloss of input of vehicle camera, LiDAR or other sensory data thatimpacts engaged driver roadway departure warning and lane-keepingassistance features.

In at least one exemplary embodiment, a method for operating a vehicleis provided. The method includes receiving, by a processor, vehiclestate data and vehicle environment data; in response to engaging of atleast one assist feature of a vehicle, determining, by the processor,whether the vehicle is operating in an upcoming road segment with atleast a low visibility condition based on the vehicle state data and thevehicle environment data to cause a loss of at least one input of sensordata to a vehicle controller that operates the at least one assistfeature; in response to the loss of at least one input of sensor data,activating, by the processor, one or more adaptive alerts based on aroad departure risk of the vehicle, and driver use of the at least oneassist feature in the upcoming road segment, wherein the road departurerisk is determined by: calculating a road departure risk index thatcompares an estimated vehicle path based on the vehicle state data witha probabilistic vehicle path for the upcoming road segment; andpredicting whether the vehicle based on the vehicle state data and adifference in a predicted level of error formulated by a calculation ofthe road departure risk index will operate within an acceptable path inthe upcoming road segment; and tracking, by the processor, the vehiclein the upcoming road segment based on vehicle navigation data to provideat least one adaptive alert of one or more adaptive alerts based on aprediction of the lane departure risk on the estimated vehicle path inthe upcoming lane segment.

In at least one exemplary embodiment, the method includes calculating bythe processor, the road departure risk index with the loss of at leastone input of sensor data including image sensor data from a vehiclecamera.

In at least one exemplary embodiment, the method includes estimating, bythe processor, a road departure risk in an upcoming roadway segmentbased on the vehicle navigation data, the estimated vehicle path, andthe vehicle state data with the loss of image sensor data from thevehicle camera caused by a weather condition.

In at least one exemplary embodiment, the method includes in response todetermining of the low visibility condition, alerting, by the processorvia the at least one adaptive alert of one or more adaptive alerts, forthe avoidance of a road excursion action by the driver in the upcomingroad segment.

In at least one exemplary embodiment, the method includes escalating, bythe processor, at least one adaptive alert of one or more adaptivealerts based on calculations of the road departure risk index whileoperating in the upcoming road segment.

In at least one exemplary embodiment, the method includes systematicallyproviding, by the processor, information via at least one adaptive alertabout the road departure risk based on the road departure risk index inthe upcoming roadway segment.

In at least one exemplary embodiment, the method includes wherein atleast one adaptive alert includes at least one icon indicatingactivation of one or more adaptive alerts based on a road departurerisk, and a low visibility mode of vehicle operation wherein the lowvisibility mode of vehicle operation includes the loss of input of imagesensor data to the vehicle controller.

In at least one exemplary embodiment, the method includes wherein thevehicle navigation data includes global navigation satellite system(GNSS) data that includes data of at least roadway segment curvature.

In at least one exemplary embodiment, the method includes wherein atleast one adaptive alert is configured when displayed to incorporateroad condition information and to change when displayed in accordancewith a road departure risk associated with at least a roadway surfacecondition.

In at least one exemplary embodiment, the method includes configuring,by the processor, at least one adaptive alert by a preference of thedriver; and adjusting, by the processor, the at least one adaptive alertbased on a perceived risk by the driver in an operating environment, andby the preference of the driver of a vehicle distance to a roadway edgein an upcoming roadway segment.

In at least one exemplary embodiment, the method includes displaying, bythe processor, on a vehicle navigation display a location for stoppageof the vehicle until the low visibility condition is improved or thevehicle is no longer operating in a low visibility mode of vehicleoperation.

In at least one exemplary embodiment, the method includes in response todetermining the low visibility condition, notifying, by the processorvia at least one adaptive alert, that the lane assist feature is notoperable and presenting an option to enable a navigation vehicleguidance assist feature.

In another exemplary embodiment, a vehicle is provided.

The vehicle includes at least one sensor that provides sensor datawithin a vehicle environment as vehicle environment data and about avehicle state as vehicle state data; and a controller that, with aprocessor and based on the sensor data, is configured to: receive thevehicle state data and the vehicle environment data; determine, inresponse to engagement of at least one assist feature of the vehicle,whether the vehicle is operating in an upcoming road segment with atleast a low visibility condition based on the vehicle state data and thevehicle environment data to cause a loss of at least one input of sensordata to a vehicle controller that operates the at least one assistfeature; activate, in response to the loss of at least one input ofsensor data, one or more adaptive alerts based on a road departure riskof the vehicle, and driver use of the at least one assist feature in theupcoming road segment, wherein the road departure risk is determined bycalculating a road departure risk index that compares an estimatedvehicle path based on the vehicle state data with a probabilisticvehicle path for the upcoming road segment, and predict whether thevehicle based on the vehicle state data and a difference in a predictedlevel of error formulated by a calculation of the road departure riskindex will operate within an acceptable path in the upcoming roadsegment; and track the vehicle in the upcoming road segment based onvehicle navigation data to provide at least one adaptive alert of one ormore adaptive alerts based on a prediction of the road departure risk onthe estimated vehicle path in the upcoming road segment.

In at least one exemplary embodiment, the vehicle includes wherein thecontroller is configured to: calculate the road departure risk indexwith the loss of at least one input of sensor data including imagesensor data from a vehicle camera.

In at least one exemplary embodiment, the vehicle includes wherein thecontroller is configured to: estimate a road departure risk in anupcoming roadway segment based on the vehicle navigation data, theestimated vehicle path, and the vehicle state data with the loss ofimage sensor data from the vehicle camera caused by a weather condition.

In at least one exemplary embodiment, the vehicle includes an alert viaat least one adaptive alert of one or more adaptive alerts in responseto a determination of the low visibility condition for the avoidance ofa road excursion action by the driver in the upcoming road segment.

In at least one exemplary embodiment, the vehicle includes wherein thecontroller is configured to: escalate the at least one adaptive alert ofone or more adaptive alerts based on calculations of the road departurerisk index while operating in the upcoming road segment; and provideinformation via the at least one adaptive alert about the road departurerisk based on the road departure risk index in the upcoming roadwaysegment.

In at least one exemplary embodiment, the vehicle includes wherein atleast one adaptive alert includes at least one icon indicatingactivation of one or more adaptive alerts based on a road departurerisk, and a low visibility mode of vehicle operation wherein the lowvisibility mode of vehicle operation includes the loss of input of imagesensor data to the vehicle controller; wherein the vehicle navigationdata includes global navigation satellite system (GNSS) data thatincludes data of at least roadway segment curvature; wherein the atleast one adaptive alert is configured when displayed to incorporateroad condition information and to change when displayed in accordancewith the road departure risk associated with at least a roadway surfacecondition.

In at least one exemplary embodiment, the vehicle includes wherein thecontroller is configured to: configure at least one adaptive alert by apreference of the driver; and adjust the at least one adaptive alertbased on a perceived risk by the driver in an operating environment, andby the preference of the driver of a vehicle distance to a roadway edgein an upcoming roadway segment.

In yet another exemplary embodiment, a system is provided. The systemincludes a processing unit disposed in a vehicle including one or moreprocessors configured by programming instructions encoded onnon-transient computer-readable media, the processing unit configuredto: receive vehicle state data and vehicle environment data; determine,in response to engagement of at least one assist feature of the vehicle,whether the vehicle is operating in an upcoming road segment with atleast a low visibility condition based on the vehicle state data and thevehicle environment data to cause a loss of at least one input of sensordata to a vehicle controller that operates the at least one assistfeature; activate, in response to the loss of at least one input ofsensor data, one or more adaptive alerts based on a road departure riskof the vehicle, and driver use of the at least one assist feature in theupcoming road segment, wherein the road departure risk is determined bycalculating a road departure risk index that compares an estimatedvehicle path based on the vehicle state data with a probabilisticvehicle path for the upcoming road segment, and predict whether thevehicle will operate within an acceptable path in the upcoming roadsegment; and track the vehicle in the upcoming road segment based onvehicle navigation data to provide at least one adaptive alert of one ormore adaptive alerts based on a prediction of the road departure risk onthe estimated vehicle path in the upcoming road segment.

BRIEF DESCRIPTION OF THE DRAWINGS

The exemplary embodiments will hereinafter be described in conjunctionwith the following drawing figures, wherein like numerals denote likeelements, and wherein:

FIG. 1 illustrates a functional block diagram of an exemplary autonomousvehicle for use with the adaptive driver notification system, inaccordance with various embodiments;

FIG. 2 illustrates a functional block diagram of a (backendcommunication) system having one or more autonomous vehicles of FIG. 1 ,in accordance with various embodiments;

FIG. 3 illustrates an exemplary diagram of an autonomous driving systemthat includes the adaptive driver notification system of the autonomousvehicle, in accordance with various embodiments;

FIG. 4 illustrates an exemplary external object calculation module(EOCM) of the adaptive drive notification system in accordance withvarious embodiments;

FIG. 5A illustrates a flow diagram of error prediction of the adaptivedriver notification system in accordance with various embodiments;

FIG. 5B illustrates a flow diagram of a reiterative step to determine anacceptable safe boundary for the predictive error to ensure the vehicleis within the defined bounded error of the adaptive driver notificationsystem in accordance with various embodiments;

FIG. 5C illustrates an exemplary diagram of the historical data and theprediction horizon for a vehicle tracking and the probabilistic measureof the vehicle tracking of the adaptive notification system inaccordance with various embodiments;

FIG. 6A illustrates an exemplary scenario of a use case of a lowvisibility event and a risk factor caused by surface friction of theadaptive driver notification system in accordance with an exemplaryembodiment;

FIG. 6B illustrates an exemplary scenario of a use case of a lowvisibility event and a risk factor caused by high road curvature of theadaptive driver notification system in accordance with an exemplaryembodiment;

FIG. 6C illustrates an exemplary scenario of a use case of a lowvisibility event and a risk factor caused by sudden ending of theroadway of the adaptive driver notification system in accordance with anexemplary embodiment;

FIG. 7 illustrates an exemplary set of graphs that depict comparisons ofan upcoming road curvature to the vehicle path prediction and the roaddeparture risk calculated based on the risk index formula of theadaptive driver notification system in accordance with variousembodiments;

FIG. 8 is an exemplary diagram of the estimated predicted error withdriver feedback and display on a heads-up display of the adaptive drivernotification system in accordance with various embodiments; and

FIG. 9 is an exemplary flowchart of the process for calculating the roaddeparture risk index without camera input, and calculations of theestimated vehicle path and desired path using navigation data andvehicle dynamics and generating intelligent warnings of the adaptivedriver notification system in accordance with various embodiments.

DETAILED DESCRIPTION

The following detailed description is merely exemplary in nature and isnot intended to limit the application and uses. Furthermore, there is nointention to be bound by any expressed or implied theory presented inthe preceding technical field, background, brief summary, or thefollowing detailed description. As used herein, the term module refersto any hardware, software, firmware, an electronic control component,processing logic, and/or processor device, individually or in anycombination, including without limitation: application-specificintegrated circuit (ASIC), an electronic circuit, a processor (shared,dedicated, or group) and memory that executes one or more software orfirmware programs, a combinational logic circuit, and/or other suitablecomponents that provide the described functionality.

Embodiments of the present disclosure may be described herein in termsof functional and/or logical block components and various processingsteps. It should be appreciated that such block components may berealized by any number of hardware, software, and/or firmware componentsconfigured to perform the specified functions. For example, anembodiment of the present disclosure may employ various integratedcircuit components, e.g., memory elements, digital signal processingelements, logic elements, look-up tables, or the like, which may carryout a variety of functions under the control of one or moremicroprocessors or other control devices. In addition, those skilled inthe art will appreciate that embodiments of the present disclosure maybe practiced in conjunction with any number of systems and that thesystems described herein are merely exemplary embodiments of the presentdisclosure.

For the sake of brevity, conventional techniques related to signalprocessing, data transmission, signaling, control, and other functionalaspects of the systems (and the individual operating components of thesystems) may not be described in detail herein. Furthermore, theconnecting lines shown in the various figures contained herein areintended to represent example functional relationships and/or physicalcouplings between the various elements. It should be noted that manyalternative or additional functional relationships or physicalconnections may be present in an embodiment of the present disclosure.

Weather conditions can affect ADAS's capabilities to sense the drivingenvironment. For example, in low visibility driving scenarios (e.g.,heavy fog, snowstorms) certain sensors such as camera and LiDAR may notbe available to assist the driver causing some active safety features tonot be enabled. It is desired to provide smart roadway departurewarnings and road-keeping navigation assists in inclement weather andlow visibility to reduce the risk of driving off the road, especially onhigh curvature sections of the road.

In exemplary embodiments, the present disclosure describes themethodology of the method of risk assessment for inclement weatherconditions that enables the lane departure warning or lane keep assistfeature to alert the driver and provide notification of possible roaddeparture even when visibility is low and camera/LiDAR sensor inputs arenot available. Also described is a process that enables escalation basedon precise localization and mapping, driver state, vehicle state, andenvironmental conditions using the calculated time to road departure andrisk index formula

In exemplary embodiments, the present disclosure describes systems,methods, and apparatuses of an adaptive driver notification process thataddresses driver assist feature availability when likely to be highlyrelied upon by the vehicle driver such as when operating the vehicleunder poor visibility conditions and providing the driver with alertescalations of losses of the driver assist feature availability that isadjusted through calculations from a risk index formula based on alikely loss of input of sensed data caused by the poor visibilityconditions. The adaptive alert notification process may be configured bysystematically predicting vehicle actions of a road departure risk underinclement weather conditions that may result in enhanced driver abilityto ensure vehicle safe operations combined with more state of mindcomfort offered to the driver when operating the vehicle under suchconditions.

In exemplary embodiments, the present disclosure describes systems,methods, and apparatuses of an adaptive driver notification process thatincorporates communication back to the driver with displayed on-screenicons showing smart adaptive notification with road departure warningthat incorporates communication back to the driver with an icon showinglow visibility mode, incorporates (i.e., GOOGLE®, APPLE® map) vehiclemapping and GNSS for precise positioning as well as information on roadcurvature and bank angle, incorporates road conditions and adjusts alertnotifications based on risk (visibility, weather, surface friction),learns driver preferences and adjusts inclement weather notificationsbased on perceived risk to environmental and dynamic factors such asdesired distance to the road edge, indicates to the driver vianavigation system and OnStar the location of the safest spot to pullover until visibility improves.

In exemplary embodiments, the present disclosure describes systems,methods, and apparatuses of an adaptive driver notification process thatnotifies the driver if the visibility is low and the vehicle cameracannot assist the driver in a lane-keeping functionality enabling thecustomer with the option to use the navigation assistance.

FIG. 1 illustrates a block diagram depicting an example vehicle 10 thatmay include a processor 44 that implements a lane-centering system 100.In general, input data is received in the lane centering system (orsimply “system”) 100. System 100 determines a process for blendinghands-on steering of a vehicle path to transitioning to an automatedhands-off lane-centering operation based on the input data received.

As depicted in FIG. 1 , vehicle 10 generally includes a chassis 12, abody 14, front wheels 16, and rear wheels 18. Body 14 is arranged onchassis 12 and substantially encloses components of vehicle 10. Body 14and chassis 12 may jointly form a frame. The vehicle wheels 16-18 areeach rotationally coupled to the chassis 12 near a respective corner ofthe body 14. Vehicle 10 is depicted in the illustrated embodiment as apassenger car, but it should be appreciated that any other vehicle,including motorcycles, trucks, sport utility vehicles (SUVs),recreational vehicles (RVs), marine vessels, aircraft, etc., can also beused.

As shown, vehicle 10 generally includes a propulsion system 20, atransmission system 22, a steering system 24, a brake system 26, asensor system 28, an actuator system 30, at least one data storagedevice 32, at least one controller 34, and a communication system 36.The propulsion system 20 may, in this example, includes an electricmachine such as a permanent magnet (PM) motor. The transmission system22 is configured to transmit power from the propulsion system 20 to thevehicle wheels 16 and 18 according to selectable speed ratios.

The brake system 26 is configured to provide braking torque to thevehicle wheels 16 and 18. Brake system 26 may, in various exemplaryembodiments, include friction brakes, brake by wire, a regenerativebraking system such as an electric machine, and/or other appropriatebraking systems.

The steering system 24 influences the position of the vehicle wheels 16and/or 18. While depicted as including a steering wheel 25 forillustrative purposes, in some exemplary embodiments contemplated withinthe scope of the present disclosure, the steering system 24 may notinclude a steering wheel.

The sensor system 28 includes one or more sensing devices 40 a-40 n thatsense observable conditions of the exterior environment and/or theinterior environment of the vehicle 10 and generate sensor data relatingthereto.

The actuator system 30 includes one or more actuator devices 42 a-42 nthat control one or more vehicle features such as, but not limited to,the propulsion system 20, the transmission system 22, the steeringsystem 24, and the brake system 26. In various exemplary embodiments,vehicle 10 may also include interior and/or exterior vehicle featuresnot illustrated in FIG. 1 , such as various doors, a trunk, and cabinfeatures such as air, music, lighting, touch-screen display components,and the like.

The data storage device 32 stores data that can be used in controllingthe vehicle 10. The data storage device 32 may be part of controller 34,separate from controller 34, or part of controller 34 and part of aseparate system.

The controller 34 (i.e., vehicle controller) includes at least oneprocessor 44 (integrate with system 100 or connected to the system 100)and a computer-readable storage device or media 46. The processor 44 maybe any custom-made or commercially available processor, a centralprocessing unit (CPU), a graphics processing unit (GPU), anapplication-specific integrated circuit (ASIC) (e.g., a custom ASICimplementing a neural network), a field-programmable gate array (FPGA),an auxiliary processor among several processors associated with thecontroller 34, a semiconductor-based microprocessor (in the form of amicrochip or chipset), any combination thereof, or generally any devicefor executing instructions. The computer-readable storage device ormedia 46 may include volatile and non-volatile storage in read-onlymemory (ROM), random-access memory (RAM), and keep-alive memory (KAM),for example. KAM is a persistent or non-volatile memory that may be usedto store various operating variables while processor 44 is powered down.The computer-readable storage device or media 46 may be implementedusing any of a number of known memory devices such as PROMs(programmable read-only memory), EPROMs (electrically PROM), EEPROMs(electrically erasable PROM), flash memory, or any other electric,magnetic, optical, or combination memory devices capable of storingdata, some of which represent executable instructions, used by thecontroller 34 in controlling the vehicle 10.

The instructions may include one or more separate programs, each ofwhich includes an ordered listing of executable instructions forimplementing logical functions. The instructions, when executed by theprocessor 44, receive and process signals (e.g., sensor data) from thesensor system 28, perform logic, calculations, methods, and/oralgorithms for automatically controlling the components of the vehicle10, and generate control signals that are transmitted to the actuatorsystem 30 to automatically control the components of the vehicle 10based on the logic, calculations, methods, and/or algorithms. Althoughonly one controller 34 is shown in FIG. 1 , embodiments of the vehicle10 may include any number of controllers 34 that communicate over anysuitable communication medium or a combination of communication mediumsand that cooperate to process the sensor signals, perform logic,calculations, methods, and/or algorithms, and generate control signalsto automatically control features of the vehicle 10.

As an example, system 100 may include any number of additionalsub-modules embedded within controller 34 which may be combined and/orfurther partitioned to similarly implement systems and methods describedherein. Additionally, inputs to the system 100 may be received from thesensor system 28, received from other control modules (not shown)associated with the vehicle 10, and/or determined/modeled by othersub-modules (not shown) within the controller 34 of FIG. 1 .Furthermore, the inputs might also be subjected to preprocessing, suchas sub-sampling, noise-reduction, normalization, feature-extraction,missing data reduction, and the like.

An autonomous system may include a Level Four system which indicates“high automation”, referring to the driving mode-specific performance byan automated driving system of all aspects of the dynamic driving task,even if a human driver does not respond appropriately to a request tointervene; and a Level Five system which indicates “full automation”,referring to the full-time performance by an automated driving system ofall aspects of the dynamic driving task under all roadway andenvironmental conditions that can be managed by a human driver.

FIG. 2 illustrates an exemplary embodiment of an operating environmentshown generally at 50 that includes an autonomous vehicle-based remotetransportation system 52 that is associated with one or more autonomousvehicles 10 a-10 n as described with regard to FIG. 1 . In variousembodiments, the operating environment 50 further includes one or moreuser devices 54 that communicate with the autonomous vehicle 10 and/orthe remote transportation system 52 via a communication network 56.

The communication network 56 supports communication as needed betweendevices, systems, and components supported by the operating environment50 (e.g., via tangible communication links and/or wireless communicationlinks). For example, the communication network 56 can include a wirelesscarrier system 60 such as a cellular telephone system that includes aplurality of cell towers (not shown), one or more mobile switchingcenters (MSCs) (not shown), as well as any other networking componentsrequired to connect the wireless carrier system 60 with a landcommunications system. Each cell tower includes sending and receivingantennas and a base station, with the base stations from different celltowers being connected to the MSC either directly or via intermediaryequipment such as a base station controller. The wireless carrier system60 can implement any suitable communications technology, including, forexample, digital technologies such as CDMA (e.g., CDMA2000), LTE (e.g.,4G LTE or 5G LTE), GSM/GPRS, or other current or emerging wirelesstechnologies. Other cell towers/base station/MSC arrangements arepossible and could be used with the wireless carrier system 60. Forexample, the base station and cell tower could be co-located at the samesite or they could be remotely located from one another, each basestation could be responsible for a single cell tower or a single basestation could service various cell towers, or various base stationscould be coupled to a single MSC, to name but a few of the possiblearrangements.

Apart from including the wireless carrier system 60, a second wirelesscarrier system in the form of a satellite communication system 64 can beincluded to provide uni-directional or bi-directional communication withthe autonomous vehicles 10 a-10 n. This can be done using one or morecommunication satellites (not shown) and an uplink transmitting station(not shown). Uni-directional communication can include, for example,satellite radio services, wherein programming content (news, music,etc.) is received by the transmitting station, packaged for upload, andthen sent to the satellite, which broadcasts the programming tosubscribers. Bi-directional communication can include, for example,satellite telephony services using the satellite to relay telephonecommunications between vehicle 10 and the station. The satellitetelephony can be utilized either in addition to or in lieu of thewireless carrier system 60.

A land communication system 62 may further be included that is aconventional land-based telecommunications network connected to one ormore landline telephones and connects the wireless carrier system 60 tothe remote transportation system 52. For example, the land communicationsystem 62 may include a public switched telephone network (PSTN) such asthat used to provide hardwired telephony, packet-switched datacommunications, and the Internet infrastructure. One or more segments ofthe land communication system 62 can be implemented through the use of astandard wired network, a fiber or other optical network, a cablenetwork, power lines, other wireless networks such as wireless localarea networks (WLANs), or networks providing broadband wireless access(BWA), or any combination thereof. Furthermore, the remote(transportation) system 52 need not be connected via the landcommunication system 62 but can include wireless telephony equipment sothat it can communicate directly with a wireless network, such as thewireless carrier system 60.

Although only one user device 54 is shown in FIG. 2 , embodiments of theoperating environment 50 can support any number of user devices 54,including multiple user devices 54 owned, operated, or otherwise used byone person. Each user device 54 supported by the operating environment50 may be implemented using any suitable hardware platform. In thisregard, the user device 54 can be realized in any common form factorincluding, but not limited to: a desktop computer; a mobile computer(e.g., a tablet computer, a laptop computer, or a netbook computer); asmartphone; a video game device; a digital media player; a piece of homeentertainment equipment; a digital camera or video camera; a wearablecomputing device (e.g., smartwatch, smart glasses, smart clothing); orthe like. Each user device 54 supported by the operating environment 50is realized as a computer-implemented or computer-based device havingthe hardware, software, firmware, and/or processing logic needed tocarry out the various techniques and methodologies described herein. Forexample, user device 54 includes a microprocessor in the form of aprogrammable device that includes one or more instructions stored in aninternal memory structure and applied to receive binary input to createbinary output. In some embodiments, the user device 54 includes a GPSmodule capable of receiving GPS satellite signals and generating GPScoordinates based on those signals. In other embodiments, the userdevice 54 includes cellular communications functionality such that thedevice carries out voice and/or data communications over thecommunication network 56 using one or more cellular communicationsprotocols, as are discussed herein. In various embodiments, the userdevice 54 includes a visual display, such as a touch-screen graphicaldisplay, or other display.

As can be appreciated, the subject matter disclosed herein providescertain enhanced features and functionality to what may be considered asa standard, a baseline, a semi-autonomous or an autonomous vehicle 10and/or an autonomous vehicle-based remote transportation system 52. Tothis end, an autonomous vehicle and autonomous vehicle-based remotetransportation system can be modified, enhanced, or otherwisesupplemented to provide the additional features described in more detailbelow.

In accordance with various embodiments, controller 34 implements anautonomous driving system (ADS) 70 as shown in FIG. 3 . That is,suitable software and/or hardware components of the controller 34 (e.g.,the processor 44 and the computer-readable storage device or media 46)are utilized to provide an autonomous driving system 70 that is used inconjunction with vehicle 10.

In various embodiments, the instructions of the autonomous drivingsystem 70 may be organized by function, module, or system. For example,as shown in FIG. 3 , the autonomous driving system 70 can include acomputer vision system 74, a positioning system 76, a guidance system78, and a vehicle control system 80. The positioning system 76 may be incommunication with an adaptive alert notification system 82 thatimplements various algorithms to provide a number of adaptive drivernotifications of availability of driver-assist features that includepredictions of a loss of input of vehicle sensor data caused by drivingconditions. For example, the driving conditions may includeweather-related conditions causing low visibility that can result in aloss of camera data input causing a likely dis-enablement (i.e.,temporary de-activation in a near future such an upcoming roadwaysegment or lane segment) of driver-assist features such aslane-centering or lane/roadway departure warning of the vehicle'sautonomous driving system 70 (or semi-autonomous driving systems). Ascan be appreciated, in various embodiments, the instructions may beorganized into any number of systems (e.g., combined, furtherpartitioned, etc.) as the disclosure is not limited to the presentexamples.

In various embodiments, the computer vision system 74 synthesizes andprocesses sensor data and predicts the presence, location,classification, and/or path of objects and features of the environmentof the vehicle 10. In various embodiments, the computer vision system 74can incorporate information from multiple sensors, including but notlimited to cameras, lidars, radars, and/or any number of other types ofsensors (including with loss of input of one sensor such as the camerasensor).

The positioning system 76 processes sensor data along with other data todetermine a position (e.g., a local position relative to a map, an exactposition relative to the lane of a road, vehicle heading, velocity,etc.) of the vehicle 10 relative to the environment. The guidance system78 processes sensor data along with other data to determine a path forvehicle 10 to follow. The vehicle control system 80 generates controlsignals for controlling the vehicle 10 according to the determined path.

In various embodiments, controller 34 implements machine learningtechniques to assist the functionality of controller 34, such as featuredetection/classification, obstruction mitigation, route traversal,mapping, sensor integration, ground-truth determination, prediction andestimation of trajectories, and routes in upcoming road segments and thelike.

The autonomous driving system 70 is configured to execute steering andspeed control maneuvers, amongst other possible autonomous drivingpossibilities, to avoid collisions and to move cooperatively withtracked objects based in part on the control commands. The autonomousdriving system 70 operates known autonomous vehicle control computerinstructions through a processor-based in part on the control data, asdescribed below with respect to FIG. 4 .

FIG. 4 illustrates an exemplary external object calculation module(EOCM) that enables vehicle control actions with the adaptive drivernotification system in accordance with various embodiments. In FIG. 4 ,system 70 further includes an external object calculation module (or“EOCM”) 400. The EOCM 400 is a computer system or controller that mayinclude, in general, components (not shown) such as a processor, acomputer-readable memory, and interfaces.

In embodiments, as shown in FIG. 4 , the EOCM 400 includes modules, andinterfaces, a receiver and input process 445 via a High-Definition Mapand Localization Module (HDLM), a waypoint generation module 450, roadcurvature, and the receiver and input process 455, a driver roaddeparture intent detection module 460, and a road departure riskcalculation module 465.

In embodiments, the EOCM 400 receives state and environmental vehicledata from a variety of vehicular sensors that include the sensor suite425 of the inertial measurement unit (IMU), steering angle sensor (SAS),and wheel speed sensor (WSS), the Global Positioning System (GPS) 430,and navigation data over ethernet via the infotainment 435. Further, theEOCM 400 communicates signal data to the driver notification module 470based on a formulation that calculates the lane (road) departure riskindex without (at least) camera input and sends signal data to thedriver notification module 470 to intelligently warn the driver to avoidroad excursion in poor visibility conditions. In embodiments, the EOCM400 includes other features that enable determination by software andalgorithms of various driver features states of enablement 410, withpredictions of risks such as road departure by sensed data of inclementweather condition detection 415.

In embodiments, the receiver and input process 455 updates a highfidelity map. For example, data from the sensor suite can be used toupdate a high fidelity map with information used to develop layers withwaypoints identifying selected events, the locations of the event ofloss of camera data, and the frequency with which the loss of cameradata events are encountered at the identified location. In this way,sensor suite data of the autonomous vehicle can continually providefeedback to the mapping system and the high fidelity map (of thereceiver and input process 445) can be updated as more and moreinformation is gathered.

In embodiments, the vehicle path prediction module 440 implementsintelligent algorithms for trajectory or path prediction modeling topredict the actual path of the operating vehicle based on sensed datafrom the sensor suite 425 that includes data related to vehicle lateralforces, wheel angles, wheel speed, and other vehicle dynamics.

In embodiments, the waypoint generation module 450 generates a targetwaypoint or several target waypoints for predicting vehicle trajectory,path, or route from the autonomous or semi-autonomous vehicle's currentlocation to a target waypoint or a selected area. In some examples, thepath may include several target waypoints and/or target areas that havea likelihood of input loss from a camera or other vehicle sensor whentraversed by the operating autonomous or semi-autonomous vehicle.

In embodiments. the predicted or estimated vehicle path is based on thesensor data available from the sensor suite 325 (shown in FIG. 3 ) andthe GPS 430 providing data of the current vehicle state data andlocation received using GNSS of road curvature and bank angle.

In embodiments, the driver road departure intent detection module 460detects a probabilistic determination and not a determination of thevehicle path prediction.

In embodiments, the road departure risk calculation module 465calculates the likelihood based on a formulation of the road departurerisk at a waypoint, location, or curvature of the roadway.

FIG. 5A illustrates a flow diagram of error prediction of the adaptivedriver notification system in accordance with various embodiments. InFIG. 5 , the predictive error E that is generated at block 505 (i.e.,the error prediction processer) by processing inputs from aprobabilistic vehicle path prediction

${\hat{P_{i}} = \begin{bmatrix}x_{i} \\y_{i} \\\theta_{i}\end{bmatrix}},{i = {1¨}}$n at block 515 (i.e., probability vehicle path processor) based ondriver input {circumflex over (τ)}_(D) at block 510, and from a desiredpath curvature estimation {circumflex over (D)}_(i) at block 520 (i.e.,path curvature estimator processor) based on GPS data from block 525 andNAV information from block 530. driver input. Other states of anestimated intended path (heading, curvature rate, etc.) also can bederived using a similar approach as described above for estimating thecurvature of the roadway.

FIG. 5B illustrates a flow diagram of a reiterative step to determine anacceptable safe boundary for the predictive error to ensure the vehicleis within the defined bounded error of the adaptive driver notificationsystem in accordance with various embodiments. In FIG. 5B, the variablefilter operation at block 535, and the updated algorithm at block 540defined the bounded error. Since there is uncertainty in the data ininclement weather conditions, it cannot be guaranteed that the errore_(i)→0; hence, an acceptable safe bound for error is determined via thevariable filter operation and the update to the algorithm to ensure thatthe vehicle is operating within a bounded error or assuring E<|{tildeover (E)}|.

The algorithm for error prediction is defined as {circumflex over(Ė)}=AE+B₁u+B₂g sin(θ_(i))+B₃ρ_(D)+{tilde over (E)} for calculating aroad departure risk index based on u_(c)=−K_(c)B₁ ⁻¹(

|sign(E)) and

$\begin{matrix}{u = {\underset{FB}{\underset{︸}{- {KE}}}\underset{FF}{\underset{︸}{{- \rho_{D}}\left( {{K_{us}V_{x}^{2}} + L} \right)}}}} & \underset{{Uncertainty}{measure}}{\underset{︸}{+ u_{C}}}\end{matrix}$

And the following (vehicle dynamic parameters) factors:{dot over (V)}=E(AE+B ₁ u+B ₂ g sin(θ_(i))+B ₃ρ_(D) +{tilde over (E)})u=−KE−ρ _(D)(K _(us) V _(x) ² +L)+u _(c){dot over (V)}=−KE ² +E(B ₁ u _(c) +{tilde over (E)})

In an exemplary embodiment, the virtual compensator is configured asu_(c)=−K_(c)B₁ ⁻¹(

|sign(E)).

In this case, then {dot over (V)}=−KE²−K_(c)|E|

|<0. If virtual control parameter u is approximately zero, the driver istracking the road profile, and when |u|>>0 this likely connotes or meansthe driver is not tracking the road's profile, thus causing a likelihoodor risk of road departure.

In an embodiment, the parameters of the error prediction algorithm{circumflex over (Ė)}=AE+B₁u+B₂g sin(θ_(i))+B₃ρ_(D)+{tilde over (E)} areupdated and are defined as follows: K_(c): design parameter—controllergain E: predictive error, A, B₁, B₂, B₃: Vehicle lateral dynamicsparameters are as follows: u: vehicle road wheel angle, θ_(i): road bankangle, ρ_(D): road curvature {circumflex over (D)}_(i): desired pathdata from a navigation system, and {circumflex over (P)}_(i):probabilistic vehicle path data.

FIG. 5C illustrates an exemplary diagram of the historical data and theprediction horizon for a vehicle tracking and the probabilistic measure(as opposed to a deterministic measure) of the vehicle tracking of theadaptive notification system in accordance with various embodiments. Thedesired path curvature estimated is path 560 based on the updatedalgorithm {circumflex over (Ė)}=AE+B₁u+B₂g sin(θ_(i))+B₃ρ_(D)+{tildeover (E)} and the probabilistic vehicle path prediction is path 570 witha region 555 of

${\hat{P_{i}} = \begin{bmatrix}x_{i} \\y_{i} \\\theta_{i}\end{bmatrix}},{i = {1¨n}}$for probabilistic path predictions. The uncertainty calculated is thedifference between both paths that is compensated for by u_(c)=−K_(c)B₁⁻¹(

|sign(E)).

FIG. 6A depicts an exemplary scenario of a use case of a low visibilityevent and a risk factor caused by surface friction of the adaptivedriver notification system in accordance with an exemplary embodiment.In FIG. 6A, in the use scenario depicted, the road edge and road markingin scenario 605 are obscured by inclement weather such as snow, fog, andheavy rain, causing a probabilistic path prediction 600 based on therisk factors incorporated of edges and marking obscured in the riskindex formula.

FIG. 6B depicts an exemplary scenario of a use case of a low visibilityevent and a risk factor caused by high road curvature of the adaptivedriver notification system in accordance with an exemplary embodiment.In FIG. 6B, in the use scenario depicted, the road edge is notdetectable due to the high road curvature (the steep bank of the roadwaymay also be a factor) in scenario 610 causing a probabilistic pathprediction 615 based on the risk factors incorporated of road edge notbeing detectable in the risk index formula.

FIG. 6C depicts an exemplary scenario of a use case of a low visibilityevent and a risk factor caused by sudden road ending of the adaptivedriver notification system in accordance with an exemplary embodiment.In FIG. 6C, in the use scenario depicted, the road edge is notdetectable due to the T-shaped intersection in scenario 630 causing aprobabilistic path prediction 625 based on the risk factors incorporatedof road edge not being detectable in the risk index formula.

In embodiments, the road trajectory and probabilistic path predictioncan be shown in a heads-up display (HUD), and a voice-over can also beimplemented. Both features may also be user touch selectable foractuation using various on-board actuation buttons for displaying theHUD, voice notifications, and other visual and audible notificationswith the adaptive driver notification system.

FIG. 7 illustrates an exemplary set of graphs that depict comparisons ofan upcoming road curvature to the vehicle path prediction and the roaddeparture risk calculated based on the risk index formula of theadaptive driver notification system in accordance with variousembodiments. In FIG. 7 , the vehicle 700 is shown operating in anupcoming road curvature and the path 710 of the desired vehicle pathbased on the estimations of the upcoming curvature using NAV informationcompared with a predicted vehicle path curvature 720 based on the riskindex formula. Graph 760 depicts comparisons of the estimated upcomingcurvature 715 of the roadway based on GPS data to the predicted vehiclepath curvature 725 based on the risk index formula. Graph 760 depictsthe road departure risk 730 that is calculated in comparison to thevehicle states and dynamics depicted in graph 750 of estimations of theroad curvature and predictions of the vehicle path. The road departurerisk in graph 760 is shown to be greatest at approximately a time of 130seconds at 735 from the current vehicle position based on data about thevehicle state and dynamics received by the adaptive driver notificationsystem and calculations based on the risk index formula and environmentfactors. A mapping display 770 is depicted that may be available to thedriver of the predicted paths and estimated road path curvature, as wellas audible or visual notifications 780 that are tied and escalated inaccordance with the road departure risk amounts of graph 760.

FIG. 8 is an exemplary diagram of the estimated predicted error withdriver feedback and display on a heads-up display of the adaptive drivernotification system in accordance with various embodiments. In FIG. 8 ,the predictive error 810 is shown with the estimated vehicle path 815based on vehicle states to the desired path 820 from the NAV feedbackfor the oncoming curvature, and driver feedback 830 throughnotifications of use of features for lane-keeping assist and curve aheadwarnings on the map and a corresponding HUD 840 of both paths to thedriver view.

FIG. 9 is an exemplary flowchart of the process for calculating the roaddeparture risk index without camera input, and calculations of theestimated vehicle path and desired path using navigation data andvehicle dynamics and generating intelligent warnings of the adaptivedriver notification system in accordance with various embodiments. Ascan be appreciated in light of the disclosure, the order of operationwithin the method is not limited to the sequential execution asillustrated in FIG. 9 but may be performed in one or more varying ordersas applicable and in accordance with the present disclosure. In variousembodiments, the method can be scheduled to run based on one or morepredetermined events (such as the loss of an input of sensor data),and/or can run continuously during the operation of vehicle 10.

The method may begin at 905. At step 910, the driver executes a requestto engage the autonomous mode for the autonomous operation of thevehicle. At the time of the driver's request, in step 910, the vehiclemay be entering a curvature of a roadway and/or experiencing inclementweather conditions that result in a low visibility type scenario inwhich the driver is likely to rely on driver-assist features such aslane departure warning or lane keep assist features based ondriver-based operating preferences, the driver state, the vehicle state,and the current environment condition. In embodiments, the driver may beoperating the vehicle in an autonomous or semi-autonomous driving modeor such mode may be desired to be activated (e.g., prior to entering acurved roadway segment).

At step 910, a driver after engaging a driver assist lane-keeping orlane departure warning feature, a suite of vehicle sensors provides to avehicle controller vehicle state data and vehicle environment data. Thevehicle controller is configured to determine whether the vehicle isoperating in an upcoming road segment with inclement weather that causesa low visibility condition based on the vehicle state data and thevehicle environment data. This may cause a loss of at least one input ofsensor data to a vehicle controller that operates at least one assistfeature such as a lane assist keeping feature or departure warningfeature.

At step 915, in response to the loss of at least one input of sensordata to the vehicle controller, the system is activated and may consistof a set of adaptive alerts based on a road departure risk of thevehicle, and driver use of at least one assist feature in the upcomingroad segment. The road departure risk is determined by the adaptivedriver notification system (hereinafter the “system”) calculating a roaddeparture risk index that compares an estimated vehicle path based onthe vehicle state data with a probabilistic vehicle path for theupcoming road segment, and predicting whether the vehicle based on thevehicle state data and a difference in a predicted level of errorformulated by a calculation of the road departure risk index willoperate within an acceptable path in the upcoming road segment. At step920, the vehicle controller tracks the vehicle in the upcoming roadsegment based on vehicle navigation data to provide by the system atleast one adaptive alert based on a prediction of the road departurerisk on the estimated vehicle path in the upcoming road segment.

At step 925, the vehicle controller is configured to calculate via thesystem the road departure risk index with the loss of at least one inputof sensor data such as image sensor data from a vehicle camera. At step930, the vehicle controller estimates via the system a road departurerisk in an upcoming roadway segment based on the vehicle navigationdata, the estimated vehicle path, and the vehicle state data with theloss of image sensor data from the vehicle camera caused by a weathercondition.

At step 935, the vehicle controller in response to determining the lowvisibility condition causes the system to provide at least one adaptivealert for the avoidance of a road excursion action by the driver in theupcoming road segment.

At step 940, the system escalates at least one adaptive alert of one ormore, or a plurality of adaptive alerts based on calculations of theroad departure risk index while operating in the upcoming road segment.

At step 945, the system systematically provides information via at leastone adaptive alert about the road/lane departure risk based on the roaddeparture risk index in the upcoming roadway segment. At least oneadaptive alert can be configured to include at least one icon indicatingactivation of one or more adaptive alerts based on a road/lane departurerisk, and a low visibility mode of vehicle operation wherein the lowvisibility mode of vehicle operation includes the loss of input of imagesensor data to the vehicle controller. The vehicle navigation dataincludes global navigation satellite system (GNSS) data that includesdata of at least roadway segment curvature. At least one adaptive alertis configured when displayed to incorporate road condition informationand to change when displayed in accordance with a road departure riskassociated with at least a roadway surface condition.

At step 950, the system is configured to enable at least one adaptivealert by a preference of the driver; and is adjusted to the at least oneadaptive alert based on a perceived risk by the driver in an operatingenvironment, and by the preference of the driver of a vehicle distanceto a roadway edge in an upcoming roadway segment.

At step 955, the system is configured to display on a vehicle navigationdisplay a location for stoppage of the vehicle until the low visibilitycondition is improved or the vehicle is no longer operating in a lowvisibility mode of vehicle operation.

At step 960, in response to determining the low visibility condition,the system is configured to notify via at least one adaptive alert, thatthe lane assist feature is not operable and presenting an option toenable a navigation vehicle guidance assistance feature to compensatefor the loss of the lane-keeping assist or roadway departure warningfunctions.

At step 965, once the low visibility condition is passed, or the weatherimproves so that the camera input is no longer lost to the vehiclecontroller, the system is configured to enable an action toautomatically disengage, and the driver may be notified of the changedstate of the vehicle assist functions and the outside condition that isno longer causes a low visibility mode of vehicle operation.

The foregoing detailed description is merely illustrative in nature andis not intended to limit the embodiments of the subject matter or theapplication and uses of such embodiments. As used herein, the word“exemplary” means “serving as an example, instance, or illustration.”Any implementation described herein as exemplary is not necessarily tobe construed as preferred or advantageous over other implementations.Furthermore, there is no intention to be bound by any expressed orimplied theory presented in the preceding technical field, background,or detailed description.

While at least one exemplary aspect has been presented in the foregoingdetailed description of the invention, it should be appreciated that avast number of variations exist. It should also be appreciated that theexemplary aspect or exemplary aspects are only examples, and are notintended to limit the scope, applicability, or configuration of theinvention in any way. Rather, the foregoing detailed description willprovide those skilled in the art with a convenient road map forimplementing an exemplary aspect of the invention. It is understood thatvarious changes may be made in the function and arrangement of elementsdescribed in an exemplary aspect without departing from the scope of theinvention as set forth in the appended claims.

What is claimed is:
 1. A method, comprising: receiving, by a processor,vehicle state data and vehicle environment data; in response to engagingof at least one assist feature of a vehicle, determining, by theprocessor, whether the vehicle is operating in an upcoming road segmentwith at least a low visibility condition based on the vehicle state dataand the vehicle environment data to cause a loss of at least one inputof sensor data to a vehicle controller that operates the at least oneassist feature; in response to the loss of at least one input of sensordata, activating, by the processor, one or more adaptive alerts based ona road departure risk of the vehicle, and driver use of the at least oneassist feature in the upcoming road segment, wherein the road departurerisk is determined by: calculating a road departure risk index thatcompares an estimated vehicle path based on the vehicle state data witha probabilistic vehicle path for the upcoming road segment; andpredicting, whether the vehicle, based on the vehicle state data and adifference in a predicted level of error formulated by a calculation ofthe road departure risk index will operate within an acceptable path inthe upcoming road segment; and tracking, by the processor, the vehiclein the upcoming road segment based on vehicle navigation data to provideat least one adaptive alert of the one or more adaptive alerts based ona prediction of the road departure risk on the estimated vehicle path inthe upcoming road segment.
 2. The method of claim 1, further comprising:calculating by the processor, the road departure risk index with theloss of at least one input of sensor data comprising image sensor datafrom a vehicle camera.
 3. The method of claim 2, further comprising:estimating, by the processor, the road departure risk in an upcomingroadway segment based on the vehicle navigation data, the estimatedvehicle path, and the vehicle state data with the loss of image sensordata from the vehicle camera caused by a weather condition.
 4. Themethod of claim 3, further comprising: in response to determining of thelow visibility condition, alerting, by the processor via the at leastone adaptive alert of the one or more adaptive alerts, for an avoidanceof a road excursion action by the driver in the upcoming road segment.5. The method of claim 4, further comprising: escalating, by theprocessor, the at least one adaptive alert of the one or more adaptivealerts based on calculations of the road departure risk index whileoperating in the upcoming road segment.
 6. The method of claim 5,further comprising: systematically providing, by the processor,information via the at least one adaptive alert about the road departurerisk based on the road departure risk index in the upcoming roadwaysegment.
 7. The method of claim 1, wherein the at least one adaptivealert comprises at least one icon indicating activation of the one ormore adaptive alerts based on the road departure risk, and a lowvisibility mode of vehicle operation wherein the low visibility mode ofvehicle operation comprises a loss of input of image sensor data to thevehicle controller.
 8. The method of claim 1, wherein the vehiclenavigation data comprises global navigation satellite system (GNSS) datathat includes data of at least roadway segment curvature.
 9. The methodof claim 1, wherein the at least one adaptive alert is configured whendisplayed to incorporate road condition information and to change whendisplayed in accordance with the road departure risk associated with atleast a roadway surface condition.
 10. The method of claim 1, furthercomprising: configuring, by the processor, the at least one adaptivealert by a preference of the driver; and adjusting, by the processor,the at least one adaptive alert based on a perceived risk by the driverin an operating environment, and by the preference of the driver of avehicle distance to a roadway edge in an upcoming roadway segment. 11.The method of claim 1, further comprising: displaying, by the processor,on a vehicle navigation display a location for stoppage of the vehicleuntil the low visibility condition is improved or the vehicle is nolonger operating in a low visibility mode of vehicle operation.
 12. Themethod of claim 1, further comprising: in response to determining thelow visibility condition, notifying, by the processor via the at leastone adaptive alert, that a lane keep assist feature is not operable andpresenting an option to enable a navigation vehicle guidance assistfeature.
 13. A vehicle, comprising: at least one sensor that providessensor data within a vehicle environment as vehicle environment data andabout a vehicle state as vehicle state data; and a controller that, witha processor and based on the sensor data, is configured to: receive thevehicle state data and the vehicle environment data; determine, inresponse to engagement of at least one assist feature of the vehicle,whether the vehicle is operating in an upcoming road segment with atleast a low visibility condition based on the vehicle state data and thevehicle environment data to cause a loss of at least one input of sensordata to the vehicle controller that operates the at least one assistfeature; activate, in response to the loss of at least one input ofsensor data, one or more adaptive alerts based on a road departure riskof the vehicle, and driver use of the at least one assist feature in theupcoming road segment, wherein the road departure risk is determined bycalculating a road departure risk index that compares an estimatedvehicle path based on the vehicle state data with a probabilisticvehicle path for the upcoming road segment, and predict whether thevehicle based on the vehicle state data and a difference in a predictedlevel of error formulated by a calculation of the road departure riskindex will operate within an acceptable path in the upcoming roadsegment; and track the vehicle in the upcoming road segment based onvehicle navigation data to provide at least one adaptive alert of theone or more adaptive alerts based on a prediction of the road departurerisk on the estimated vehicle path in the upcoming road segment.
 14. Thevehicle of claim 13, wherein the controller is configured to: calculatethe road departure risk index with the loss of at least one input ofsensor data comprising image sensor data from a vehicle camera.
 15. Thevehicle of claim 14, wherein the controller is configured to: estimatethe road departure risk in an upcoming roadway segment based on thevehicle navigation data, the estimated vehicle path, and the vehiclestate data with the loss of image sensor data from the vehicle cameracaused by a weather condition.
 16. The vehicle of claim 15, furthercomprising: alert via the at least one adaptive alert of the one or moreadaptive alerts in response to a determination of the low visibilitycondition for an avoidance of a road excursion action by the driver inthe upcoming road segment.
 17. The vehicle of claim 16, wherein thecontroller is configured to: escalate the at least one adaptive alert ofthe one or more adaptive alerts based on calculations of the roaddeparture risk index while operating in the upcoming road segment; andprovide information via the at least one adaptive alert about the roaddeparture risk based on the road departure risk index in the upcomingroadway segment.
 18. The vehicle of claim 13, comprising: wherein the atleast one adaptive alert comprises at least one icon indicating theactivation of the one or more adaptive alerts based on the roaddeparture risk, and a low visibility mode of vehicle operation whereinthe low visibility mode of vehicle operation comprises loss of input ofimage sensor data to the vehicle controller; wherein the vehiclenavigation data comprises global navigation satellite system (GNSS) datathat includes data of at least roadway segment curvature; wherein the atleast one adaptive alert is configured when displayed to incorporateroad condition information and to change when displayed in accordancewith the road departure risk associated with at least a roadway surfacecondition.
 19. The vehicle of claim 13, wherein the controller isconfigured to: configure the at least one adaptive alert by a preferenceof the driver; and adjust the at least one adaptive alert based on aperceived risk by the driver in an operating environment, and by thepreference of the driver of a vehicle distance to a roadway edge in anupcoming roadway segment.
 20. A system comprising: a non-transitorycomputer readable storage medium storing a program; and a processingunit disposed in a vehicle comprising one or more processors configuredto execute the program, to thereby: receive vehicle state data andvehicle environment data; determine, in response to engagement of atleast one assist feature of the vehicle, whether the vehicle isoperating in an upcoming road segment with at least a low visibilitycondition based on the vehicle state data and the vehicle environmentdata to cause a loss of at least one input of sensor data to a vehiclecontroller that operates the at least one assist feature; activate, inresponse to the loss of at least one input of sensor data, one or moreadaptive alerts based on a road departure risk of the vehicle, anddriver use of the at least one assist feature in the upcoming roadsegment, wherein the road departure risk is determined by calculating aroad departure risk index that compares an estimated vehicle path basedon the vehicle state data with a probabilistic vehicle path for theupcoming road segment, and predict whether the vehicle will operatewithin an acceptable path in the upcoming road segment; and track thevehicle in the upcoming road segment based on vehicle navigation data toprovide at least one adaptive alert of the one or more adaptive alertsbased on a prediction of the road departure risk on the estimatedvehicle path in the upcoming road segment.